Browsing by Author "Ortega-Culaciati, Francisco"

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    A machine learning approach for slow slip event detection using GNSS time-series
    (2023) Donoso, Felipe; Yanez, Vicente; Ortega-Culaciati, Francisco; Moreno, Marcos
    Extracting tectonic transient displacements on the Earth's surface from Global Navigation Satellite System (GNSS) time series remains a challenge, because GNSS station displacements depend on multiple processes occurring simultaneously, along with noise that obscures low-magnitude transient signals. We present a novel method for automatic detection of slow slip events (SSEs) in time series of a GNSS network by training a supervised machine learning (ML) model for classification. The proposed methodology detects both temporally and spatially the signatures of SSEs or regional transients within a GNSS network. The time series of a GNSS network were transformed into grayscale images, from which descriptors, including Bag of Visual Words (BoW) and Extended Local Binary Patterns (ELBP), were extracted. These descriptors served as input features for two distinct ML models: Support Vector Machines (SVM) and Artificial Neural Networks (NN). To train and test the ML classification model, two 3-year synthetic datasets were generated, one with GNSS networks featuring slow slip events (SSEs) of varying location, duration, onset time, and magnitude, and the other without SSEs, resulting in positive and negative sets, respectively. For each GNSS network, an image was created by combining the east and north components of the time series, which have been previously detrended and common mode error filtered. Each image is further divided into sub-images corresponding to 60 days time windows, in order to temporarily detect the existence of a transient. For training and testing, the datasets were separated into 75% for training and 25% for testing, each with 50% positive and 50% negative cases. In the final step, we analyze the positively classified images, representing the time windows in which the classifier detected transients. Within each of these windows, we identify the network's time series with the highest velocity, indicating the stations and geographic area where the detected transients occurred. The test results demonstrate that both ML models achieved high performance using both ELBP and BoW descriptors as features. Finally, our ML models were validated on a real dataset with a transient signal recorded before the 2014 Iquique earthquake in Chile, and they effectively detected this anomalous signal. The proposed method can effectively detect transient signals related to SSEs with high accuracy, sensitivity, and specificity in both the test and instrumentally recorded datasets.
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    A supervised machine learning approach for estimating plate interface locking: Application to Central Chile
    (2024) Barra, Sebastian; Moreno, Marcos; Ortega-Culaciati, Francisco; Benavente, Roberto; Araya, Rodolfo; Bedford, Jonathan; Calisto, Ignacia
    Estimating locking degree at faults is important for determining the spatial distribution of slip deficit at seismic gaps. Inverse methods of varying complexity are commonly used to estimate fault locking. Here we present an innovative approach to infer the degree of locking from surface GNSS velocities by means of supervised learning (SL) algorithms. We implemented six different SL regression methods and apply them in the Central Chile subduction. These methods were first trained on synthetic distributions of locking and then used to infer the locking from GNSS observations. We tested the performance of each algorithm and compared our results with a least squares inversion method. Our best results were obtained using the Ridge regression, which gives a root mean square error (RMSE) of 1.94 mm/yr compared to GNSS observations. The ML -based locking degree distribution is consistent with results from the EPIC Tikhonov regularized least squares inversion and previously published locking maps. Our study demonstrates the effectiveness of machine learning methods in estimating fault locking and slip, and provides flexible options for incorporating prior information to avoid slip instabilities based on the characteristics of the training set. Exploring uncertainties in the physical model during training could improve the robustness of locking estimates in future research efforts.
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    Fast relocking and afterslip-seismicity evolution following the 2015 Mw 8.3 Illapel earthquake in Chile
    (2023) Hormazabal, Joaquin; Moreno, Marcos; Ortega-Culaciati, Francisco; Carlos Baez, Juan; Pena, Carlos; Sippl, Christian; Gonzalez-Vidal, Diego; Ruiz, Javier; Metzger, Sabrina; Yoshioka, Shoichi
    Large subduction earthquakes induce complex postseismic deformation, primarily driven by afterslip and viscoelastic relaxation, in addition to interplate relocking processes. However, these signals are intricately intertwined, posing challenges in determining the timing and nature of relocking. Here, we use six years of continuous GNSS measurements (2015-2021) to study the spatiotemporal evolution of afterslip, seismicity and locking after the 2015 Illapel earthquake (M-w 8.3). Afterslip is inverted from postseismic displacements corrected for nonlinear viscoelastic relaxation modeled using a power-law rheology, and the distribution of locking is obtained from the linear trend of GNSS stations. Our results show that afterslip is mainly concentrated in two zones surrounding the region of largest coseismic slip. The accumulated afterslip (corresponding to M-w 7.8) exceeds 1.5 m, with aftershocks mainly occurring at the boundaries of the afterslip patches. Our results reveal that the region experiencing the largest coseismic slip undergoes rapid relocking, exhibiting the behavior of a persistent velocity weakening asperity, with no observed aftershocks or afterslip within this region during the observed period. The rapid relocking of this asperity may explain the almost regular recurrence time of earthquakes in this region, as similar events occurred in 1880 and 1943.
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    Mosaicking Andean morphostructure and seismic cycle crustal deformation patterns using GNSS velocities and machine learning
    (2023) Yanez-Cuadra, Vicente; Moreno, Marcos; Ortega-Culaciati, Francisco; Donoso, Felipe; Baez, Juan Carlos; Tassara, Andres
    We use unsupervised machine learning techniques to analyze continental-scale crustal motions in areas affected by the seismic cycle of large subduction earthquakes along the Chilean Trench. Specifically, we use the agglomerative clustering algorithm as an exploratory tool to investigate spatial patterns in GNSS regional velocities without the complexity of modeling a physical source. We present a continental-scale velocity field including all available GNSS data for two-time windows (pre-2014, 2018-2021) that represents two periods with different deformation patterns of the seismic cycle. We test two different pre-processing methodologies for the design of machine learning features from the GNSS-derived velocities. The first method uses the direction and magnitude of the secular rates as input features to the clustering algorithm. These results show a clustering spatially related to seismic cycle deformation, separating latitudinal segments with different velocities in the fore-arc and back-arc, as well as regions affected by postseismic relaxation. Thus, highlighting the effectiveness of this method for mapping first-order patterns of active deformation in a subduction zone, that are particularly related to variations on interplate coupling and postseismic transient deformation. In a more sophisticated approach, we use surface strain and rotational rates from GNSS velocities as features in the second methodology. Here, we develop a novel methodology to estimate strain and rotation rates accounting for the spatial heterogeneity of the GNSS-network. We determine the spatial scale at which these features are estimated by least squares inversions, by using a Bayesian model class selection method. The distribution of stations allows to identify heterogeneities in strain and rotation rates at spatial scales larger than 50 km, being particularly notorious the main features of regional deformation at scales > 100 km. Interestingly, the results show a spatial correlation between seismic segmentation in the fore-arc and geologic and structural domains in the arc and back-arc. Our results demonstrate the ability of the combination of inverse and machine learning methods to efficiently identify active deformation patterns and their relationship to the subduction seismic cycle and regional-scale geological structures. Furthermore, our analysis suggests that Andean geological structures influence the observed deformation field.
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    Relation Between Oceanic Plate Structure, Patterns of Interplate Locking and Microseismicity in the 1922 Atacama Seismic Gap
    (2023) Gonzalez-Vidal, Diego; Moreno, Marcos; Sippl, Christian; Baez, Juan Carlos; Ortega-Culaciati, Francisco; Lange, Dietrich; Tilmann, Frederik; Socquet, Anne; Bolte, Jan; Hormazabal, Joaquin; Langlais, Mickael; Morales-Yanez, Catalina; Melnick, Daniel; Benavente, Roberto; Muenchmeyer, Jannes; Araya, Rodolfo; Heit, Benjamin
    We deployed a dense geodetic and seismological network in the Atacama seismic gap in Chile. We derive a microseismicity catalog of >30,000 events, time series from 70 GNSS stations, and utilize a transdimensional Bayesian inversion to estimate interplate locking. We identify two highly locked regions of different sizes whose geometries appear to control seismicity patterns. Interface seismicity concentrates beneath the coastline, just downdip of the highest locking. A region with lower locking (27.5 & DEG;S-27.7 & DEG;S) coincides with higher seismicity levels, a high number of repeating earthquakes and events extending toward the trench. This area is situated where the Copiapo Ridge is subducted and has shown previous indications of both seismic and aseismic slip, including an earthquake sequence in 2020. While these findings suggest that the structure of the downgoing oceanic plate prescribes patterns of interplate locking and seismicity, we note that the Taltal Ridge further north lacks a similar signature.